Data Analyst

Role Purpose

The purpose of this role is to interpret data and transform it into actionable information (reports, dashboards, interactive visualizations, etc.) that can enhance business decisions.

Key Responsibilities

• Project Management: Manage the technical scope of projects in alignment with requirements at all stages.
• Data Gathering: Collect information from various sources (data warehouses, databases, data integration, and modeling) and interpret patterns and trends.
• Record Management: Develop record management processes and policies.
• Relationship Building: Build and maintain relationships with clients at all levels and understand their requirements.
• Data Insights: Provide sales data, proposals, insights, and account reviews to clients.
• Process Improvement: Identify areas for increased efficiency and automation in processes.
• Automated Data Processes: Set up and maintain automated data processes.
• Data Validation: Identify, evaluate, and implement external services and tools for data validation and cleansing.
• Key Performance Indicators: Produce and track key performance indicators (KPIs).
• Data Analysis: Analyze datasets and provide relevant information.
• Client Liaison: Liaise with internal and external clients to fully understand data content.
• Survey Analysis: Design and conduct surveys and analyze survey data as per customer requirements.
• Complex Data Interpretation: Analyze and interpret complex data sets related to clients' businesses and prepare reports for internal and external audiences using business analytics reporting tools.
• Data Visualization: Create dashboards, graphs, and visualizations to showcase business performance, including sector and competitor benchmarking.
• Data Mining: Mine and analyze large datasets, draw valid inferences, and present findings to management using reporting tools.
• Predictive Modeling: Develop predictive models and share insights with clients as needed.

Stakeholder Interaction

Competencies Required

Functional Competencies/Skills:

• Leveraging Technology: Knowledge of current and upcoming technology (automation, tools, and systems) to build efficiencies and effectiveness. (Expert)
• Process Excellence: Ability to follow standards and norms to produce consistent results, control effectively, and reduce risk. (Expert)
• Technical Knowledge: Proficiency in various programming languages/software (Python, Microsoft Excel, VBA, MATLAB, SQL, etc.) and data analytics tools. (Expert)

Competency Levels:

• Foundation: Knowledgeable about competency requirements; demonstrates frequently with minimal support.
• Competent: Consistently demonstrates competency without guidance, extending it to difficult situations.
• Expert: Applies competency in all situations and serves as a guide to others.
• Master: Coaches others, builds organizational capability, and is recognized as a key resource.

Behavioral Competencies:

• Formulation & Prioritization
• Client Centricity
• Execution Excellence
• Passion for Results
• Confidence
• Business Acumen

Performance Metrics

1. Data Analysis: Analyze data sets and provide relevant information to clients.
   • Measures: Number of automations completed, on-time delivery, CSAT score, zero customer escalations, data accuracy.
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